ArticleBrain sciences2023
Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of brain-computer interface-based rehabilitation on lower limb function and activities of daily living after stroke: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- EEG Feature Extraction and Classification for Upper Limb Flexion and Extension Motor Imagery Based on Discriminative Filter Bank Common Spatial Pattern.Brain sciences · 2026Article
- Pre-movement EEG microstates reflect intended lifted load of volitional movement.Frontiers in computational neuroscience · 2026Article
- Polarity-considered EEG microstates improve classification accuracy of oddball stimulus.Frontiers in human neuroscience · 2026Article
- Quantifying and evaluating motor imagery ability using EEG microstates in MI-BCI training.Experimental brain research · 2025Article
- A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.Neuroscience bulletin · 2025Article
- Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.Journal of neuroengineering and rehabilitation · 2025Article
- Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.Brain and behavior · 2025Article
- Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.Frontiers in neuroscience · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.
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